[1]} use the message-passing paradigm ([2]}, [3]}, [4]}) to solve 1D PDEs. They are able to beat state-of-the-art Fourier Neural Operators [5]} and classical WENO5 solvers while introducing the "pushforward trick" that allows them to generate better long-term rollouts. Moreover, they present an added advantage over existing methods since they can learn PDE solutions at any mesh. However, they are not able to generalize to different resolutions, which is a crucial capability of our method.
